AI & Machine Learning (Optional)
AI & Machine Learning Mastery Roadmap
This detailed roadmap will take you from beginner to expert in AI and Machine Learning, covering mathematical foundations, Python libraries, supervised and unsupervised learning, deep learning, NLP, computer vision, and real-world projects.
Phase 1: Fundamentals of AI & Machine Learning
✅ Mathematical Foundations
Linear Algebra (Vectors, Matrices, Eigenvalues)
Probability & Statistics (Bayes’ Theorem, Distributions)
Calculus (Derivatives, Gradients, Optimization)
✅ Python for Machine Learning
NumPy & Pandas (Data Manipulation, Arrays)
Matplotlib & Seaborn (Data Visualization)
Scikit-learn (Basic ML Models)
📌 Mini Projects:
Exploratory Data Analysis (EDA) on a Dataset
Simple Regression Model (Predict House Prices)
Phase 2: Supervised & Unsupervised Learning
✅ Supervised Learning
Linear & Logistic Regression
Decision Trees & Random Forests
Support Vector Machines (SVM)
Gradient Boosting (XGBoost, LightGBM, CatBoost)
✅ Unsupervised Learning
Clustering (K-Means, DBSCAN, Hierarchical Clustering)
Dimensionality Reduction (PCA, t-SNE, LDA)
Anomaly Detection & Outlier Detection
📌 Mini Projects:
Customer Segmentation Using Clustering
Spam Email Classification with Logistic Regression
Phase 3: Deep Learning & Neural Networks
✅ Neural Networks & Deep Learning
Introduction to Neural Networks (Perceptron, MLPs)
Activation Functions (ReLU, Sigmoid, Softmax)
Backpropagation & Optimization (SGD, Adam, RMSProp)
✅ Deep Learning Frameworks
TensorFlow & Keras (Model Building, Training, Evaluation)
PyTorch (Tensors, Autograd, Dynamic Computation Graphs)
📌 Mini Projects:
Digit Recognition Using CNN (MNIST Dataset)
Sentiment Analysis Using LSTMs
Phase 4: Natural Language Processing (NLP)
✅ Text Processing & Feature Engineering
Tokenization, Stemming, Lemmatization
Bag-of-Words (BoW), TF-IDF, Word Embeddings
✅ Advanced NLP Models
Recurrent Neural Networks (RNN, LSTM, GRU)
Transformers (BERT, GPT, T5)
📌 Mini Projects:
Chatbot Development (Intent Recognition & Response Generation)
Text Summarization Using Transformers
Phase 5: Computer Vision & Image Processing
✅ Basic Image Processing
OpenCV (Edge Detection, Filters, Contours)
Image Augmentation & Feature Extraction
✅ Deep Learning for Computer Vision
Convolutional Neural Networks (CNNs)
Object Detection (YOLO, SSD, Faster R-CNN)
Image Segmentation (U-Net, Mask R-CNN)
📌 Mini Projects:
Face Detection & Recognition System
Object Detection for Traffic Surveillance
Phase 6: Reinforcement Learning & AI Applications
✅ Reinforcement Learning (RL)
Markov Decision Process (MDP)
Q-Learning & Deep Q Networks (DQN)
Policy Gradient Methods
✅ AI Applications & Advanced Topics
Generative AI (GANs, VAEs)
AI Ethics & Explainability (SHAP, LIME)
📌 Mini Projects:
AI Agent Playing Games (OpenAI Gym)
Image Generation Using GANs
Phase 7: AI Deployment & Model Optimization
✅ ML Model Deployment
Flask & FastAPI for Model Serving
Deploying on AWS, GCP, Heroku
✅ MLOps & Model Optimization
Model Monitoring & AutoML
Hyperparameter Tuning (Optuna, Grid Search)
📌 Mini Projects:
Deploy a Face Recognition Model on AWS Lambda
Automate ML Pipelines with Apache Airflow
Final Step: Real-World Practice & Skill Testing
🔥 Platforms to Test & Improve Skills:
Kaggle – Practice ML Challenges
Google Colab – Free Cloud GPUs for ML
Papers with Code – State-of-the-Art AI Research
🚀 By mastering this roadmap, you’ll be able to: ✅ Build & Deploy AI-Powered Applications ✅ Master NLP, Computer Vision & Reinforcement Learning ✅ Optimize & Automate AI Workflows (MLOps)
🔥 Start your AI journey today!
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